Compared to the extractive approach where a summary is constructed using extracted sentences, abstractive summarizers paraphrase the idea of the original documents in a new form, and have a potential to generate more concise and coherent summaries.
However, developing effective abstractive summarizers is harder since we have to deal with problems, like semantic representation, inference and natural language generation, which are considered more challenging than sentence extraction. Therefore, early abstractive methods underperform extractive methods, and often rely on an extractive pre-processor to produce the abstract of text [1]}, [2]}.
